Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences

作者：Wang, Wei^*; Sun, Lin; Zhang, Shiguang; Zhang, Hongjun; Shi, Jinling; Xu, Tianhe; Li, Keliang

来源：BMC Bioinformatics, 2017, 18(1): 300.

DOI：10.1186/s12859-017-1715-8

• SSBs (Single-stranded DNA-binding proteins)
• DSBs (Double-stranded DNA-binding proteins)
• Binding specificity
• Protein sequence

Background: DNA-binding proteins perform important functions in a great number of biological activities. DNA-binding proteins can interact with ssDNA (single-stranded DNA) or dsDNA (double-stranded DNA), and DNA-binding proteins can be categorized as single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs). The identification of DNA-binding proteins from amino acid sequences can help to annotate protein functions and understand the binding specificity. In this study, we systematically consider a variety of schemes to represent protein sequences: OAAC (overall amino acid composition) features, dipeptide compositions, PSSM (position-specific scoring matrix profiles) and split amino acid composition (SAA), and then we adopt SVM (support vector machine) and RF (random forest) classification model to distinguish SSBs from DSBs.
Results: Our results suggest that some sequence features can significantly differentiate DSBs and SSBs. Evaluated by 10 fold cross-validation on the benchmark datasets, our prediction method can achieve the accuracy of 88.7% and AUC (area under the curve) of 0.919. Moreover, our method has good performance in independent testing.
Conclusions: Using various sequence-derived features, a novel method is proposed to distinguish DSBs and SSBs accurately. The method also explores novel features, which could be helpful to discover the binding specificity of DNA-binding proteins.

• 出版日期2017-6-12
• 单位河南师范大学; 许昌学院